High-throughput phase field simulation and machine learning for predicting the breakdown performance of all-organic composites

Abstract All-organic dielectric polymers are materials of choice for modern power electronics and high-density energy storage, and their performance can be significantly improved by doping trace amounts of organic molecular semiconductors with strong electron-affinity energy to suppress charge conduction losses. Insight into the breakdown mechanism of polymers/organic molecular semiconductor composites is essential for the design of high-performance dielectric polymers. This study investigates the impact of the doping concentration of organic molecular semiconductors, dielectric constants, and trap depths on the breakdown performance of dielectric polymers under high temperature and electric fields. A modified phase-field model, incorporating deep traps and carriers’ coulomb capture radius, has been developed to facilitate high-throughput simulations of electrical breakdown in polymer/organic molecular semiconductor composites. This work accurately predicted the breakdown strength of all-organic composites using high-throughput phase-field simulation data as input for machine learning, which provides crucial theoretical support for designing all-organic composite dielectric polymers for energy storage capacitors under extreme conditions.